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In the last few years many Web sites were created to build social networks. These sites have common behavior, for example they need their users to search and mark their friends, so those are the users who build the graph themselves. This graph is fixed from a certain point of view: the connections are binary connections and the graph consists of only the explicit data given by the users. In this paper we define a social network based on e-mails which is distributed and asymmetric. The key differences between this type of network and usual networks are that using this network we do not need to register to a specific Web site and we do not need to mark our friends. The social graph can be built automatically based on the mass of e-mails and the address book belonging to us. By analyzing this huge amount of data we can reveal the connections between our contacts and build a weighted graph. Besides this we can create a graph from the e-mails using the same data to improve the search results when looking for a specific e-mail or contact. These weighted graphs can be considered as neural networks. We can create an adaptive method to modify the connections in the graphs with implicit data gathered from the behavior of the user. Moreover, we can define a search algorithm on this type of graph based on the activation model of the neural networks. In this paper we show a way to build this kind of networks and we define an algorithm which finds similar elements to a specific item.